Multi-Target Tracking Using Hybrid Particle Filtering

Abstract

We address the problem of multi-target tracking based on sequential Monte Carlo filtering for a visual access control application. Sequential Monte Carlo methods are very suitable for approximating posterior distributions for single target tracking applications. However, tracking multiple targets is more difficult and critically depends on the ability to represent all statistically significant modes with a sufficient number of samples. Even when tracking a single target, controlling the effective sample size of the particle set only crudely estimates how well it approximates the posterior target distribution. In contrast, previous work demonstrates that using a Kalman filter control loop, which monitors the performance of the particle filter, can dramatically improve posterior distribution approximation in a dynamic fashion. This paper extends this principle to multi-target tracking by introducing a technique called mode stratification. In addition, a method to automatically augment and delete the number of modes using local relative entropy measures is introduced. Experiments applying the proposed technique for visual head tracking in an access control application illustrate the effectiveness of the method